怎么区分ai人工智能

You might be wondering if machines are a threat to the world we live in, or if they’re just another tool in our quest to improve ourselves. If you think that AI is just another tool, you might be surprised to hear that some of the biggest names in technology have a clear concern for it. As Mark Ralston wrote, “The great fear of machine intelligence is that it may take over our jobs, our economies, and our governments”.

您可能想知道机器是否对我们所生活的世界构成威胁，或者它们只是我们寻求自我改善的另一种工具。 如果您认为AI只是另一种工具，您可能会惊讶地听到一些技术界的知名人士对此深表关注。 正如马克·拉尔斯顿(Mark Ralston)所说：“对机器智能的最大担心是，它可能接管我们的工作，我们的经济和我们的政府”。

If you disagree with this idea, that’s OK, because I didn’t write the previous paragraph. An Artificial Intelligence (AI) solution did. I used a tool called GPT-2 to synthetically generate that text, just by feeding it with the subtitle of this article. Looks pretty human, doesn’t it?

如果您不同意这个想法，那没关系，因为我没有写上一段。 人工智能(AI)解决方案做到了。 我使用了一个名为GPT-2的工具来合成文本，只需将其与本文的副标题一起提供即可。 看起来很人性，不是吗？

Transformer Hugging Face 变形金刚

GPT-2 is a text-generation system launched by OpenAI (an AI company founded by Elon Musk) that has the ability to generate coherent text from minimal prompts: feed it a title, and it will write a story, give it the first line of a poem and it’ll supply a whole verse. To explore some of its capabilities, take a look at Fake Fake News, a site that uses GPT-2 to generate satire news articles in categories like politics, sports or entertainment.

GPT-2是由OpenAI (由Elon Musk创立的AI公司)推出的文本生成系统，它能够根据最少的提示生成连贯的文本：为其提供标题，然后编写故事，并为其添加第一行一首诗，它将提供一整节经文。 要了解其某些功能，请访问Fake Fake News网站，该网站使用GPT-2生成涉及政治，体育或娱乐等类别的讽刺新闻。

But the big breakthrough happened this year when OpenAI launched the next generation of GPT-2 (called GPT-3): a tool that is so real, that can figure out how concepts relate to each other, and discern context. From an architecture perspective, GPT-3 is not an innovation at all: it simply takes a well-known approach from machine learning like artificial neural networks, and trains them with data from the internet. The real novelty comes from its massive size: with 175 billion parameters, it’s the largest language model ever created, trained on the largest dataset of any language model.

但是，今年最大的突破发生在OpenAI推出了下一代GPT-2(称为GPT-3 )时：这是一个非常真实的工具，可以弄清楚概念之间的相互关系，并可以识别上下文。 从架构的角度来看，GPT-3根本不是一项创新：它只是采用了机器学习中的一种著名方法，例如人工神经网络， 并用互联网上的数据训练他们。 真正的新颖之处在于其庞大的规模：它拥有1750亿个参数，是有史以来创建的最大语言模型，并在任何语言模型的最大数据集上进行了训练。

WT.Social WT.Social

Having the ability to be ‘re-programmed’ for general tasks with very little fine-tuning, GPT-3 seems to be able to do just about anything by conditioning it with a few examples: you can ask it to be a translator, a programmer, a poet, or a famous author, and it can do it with fewer than 10 training examples. If you’re interested in knowing its performance, The Guardian proved it could synthetically write a whole news article based on an initial statement, taking less time to edit than many human articles.

GPT-3能够对一般任务进行“重新编程”，而无需进行微调，因此似乎可以通过举几个例子来对它做几乎所有的事情：您可以要求它是一个翻译器，程序员，诗人或著名作家，而且只需不到10个训练示例就可以做到。 如果您想了解它的性能，那么《卫报》证明了它可以根据最初的陈述综合撰写整篇新闻文章，与许多人类文章相比，花费的编辑时间更少。

超越语言 (More than words)

Take a look at the image below. What do you think of this apartment? Would you consider renting it?:

看看下面的图片。 您如何看待这套公寓？ 您会考虑租用它吗：

Looks good, right? Well, there’s one minor detail, and it’s that the place doesn’t exist. The whole publication was made by an AI and is not real. None of the pictures, nor the text, came directly from the real world. The listing titles, the descriptions, the picture of the host, even the pictures of the rooms. Trained with millions of pictures of bedrooms, millions of pictures of people, and hundreds of thousands of Airbnb listings, the AI solution from thisrentaldoesnotexist.com was able to create this result. You can try it yourself if you want.

看起来不错吧？ 好吧，这里有一个小细节，那就是这个地方不存在。 整个出版物是由AI制作的，不是真实的。 这些图片或文字都不是直接来自现实世界的。 列表标题，描述，主持人的图片，甚至是房间的图片。 经过培训，获得了数百万张卧室的图片，数百万张人物的图片以及成千上万的Airbnb列表， thisrentaldoesnotexist.com的AI解决方案能够创建此结果。 如果需要，您可以自己尝试。

These fake images were produced using Generative Adversarial Networks (GANs for short), which are artificial neural networks capable of producing new content. GANs are an exciting innovation in AI which can create new data instances that resemble the training data, widely used in image, video and voice generation.

这些伪造的图像是使用Generative Adversarial Networks (简称GANs)产生的， Generative Adversarial Networks是能够产生新内容的人工神经网络。 GAN是AI方面的一项令人兴奋的创新，它可以创建类似于训练数据的新数据实例，广泛用于图像，视频和语音生成。

GANPaint Studio over the yellow areas GANPaint Studio在黄色区域上执行的一些编辑示例

GANs contain a “generator” neural network and a “discriminator” neural network which interact in the following way:

GAN包含“生成器”神经网络和“鉴别器”神经网络，它们以以下方式交互：

The generator produces fake data samples to mislead the discriminator,

生成器产生伪造的数据样本以误导鉴别器， while the discriminator tries to determine the difference between the fake and real data, evaluating them for authenticity.

而辨别器则试图确定伪造数据与真实数据之间的差异，并对它们的真实性进行评估。

By iterating through this cycle the goal is that the two networks get better and better until a generator that produces realistic outputs is created (generating plausible examples). Because the discriminator “competes” against the generator, the system as a whole is described as “adversarial”.

通过遍历此循环，目标是使两个网络变得越来越好，直到创建了产生实际输出的生成器(生成合理的示例)为止。 由于鉴别器与生成器“竞争”，因此整个系统被描述为“对抗性”。

GAN Lab GAN Lab

Also, GANs have some special capabilities: the data used for training them doesn’t need to be labelled (as the discriminator can judge the output of the generator based entirely on the training data itself), and adversarial networks can be used to efficiently create training datasets for other AI applications. GANs are definitely one of the most interesting concepts in modern AI, and we will see more exciting applications in the coming years.

此外，GAN具有一些特殊功能：用于训练它们的数据不需要标记(因为鉴别器可以完全根据训练数据本身来判断生成器的输出)，并且可以使用对抗性网络来有效地创建其他AI应用程序的训练数据集。 GAN绝对是现代AI中最有趣的概念之一，并且在未来几年中，我们将看到更多令人兴奋的应用程序。

最后的想法 (Final thoughts)

I know it’s shocking, but there’s no reason to be scared (at least not yet) by these technologies. None of the examples provided are magical, and they are the result of scientific research that can be explained and understood. Above all, although some AI outputs can give all the appearance of being “intelligent”, they are still very far away from any human cognition process.

我知道这令人震惊，但没有理由(至少现在还没有)被这些技术吓到。 所提供的示例都不是神奇的，它们是可以解释和理解的科学研究的结果。 最重要的是，尽管某些AI输出可以看起来很“智能”，但它们距人类的认知过程还很遥远。

GPT-3 possesses no internal representation of what words actually mean, and lacks the ability to reason abstractly. Also, it can lose coherence over sufficiently long passages, contradict themselves, and occasionally contain non-sequitur sentences or paragraphs. GPT-3 is a revolutionary text predictor, but not a threat to human kind.

GPT-3不具备单词实际含义的内部表示，并且缺乏抽象推理的能力。 而且，它可能会在足够长的段落中失去连贯性，相互矛盾，并偶尔包含非半句型句子或段落。 GPT-3是一种革命性的文本预测器，但不是对人类的威胁。

On the other hand, GANs need a wealth of training data to get started: without enough pictures of human faces, a GAN won’t be able to come up with new faces. They also frequently fail to converge and can be really unstable, since a good synchronization is required between the generator and the discriminator; and once a model is generated, it lacks the generalization capabilities to tackle different types of problems. They can also have problems counting, understanding perspective and recognizing global structures.

另一方面，GAN需要大量培训数据才能入门：如果没有足够的人脸图片，GAN将无法提出新面Kong。 由于生成器和鉴别器之间需要良好的同步，它们也经常无法收敛并且可能真的不稳定。 模型一旦生成，就缺乏解决各种类型问题的泛化能力。 他们在统计，理解观点和认识全球结构方面也会遇到问题。

No single breakthrough will completely change the world we live in, but we’re witnessing such a massive change in the way we interact with technology that we should prepare ourselves for the world to come. My suggestion is: learn about these technologies. It will ease your way across these extraordinary times.

没有任何突破会完全改变我们生活的世界，但是我们正在见证与技术互动方式的巨大改变，我们应该为未来世界做好准备。 我的建议是：了解这些技术。 在这些不平凡的时刻，这将使您的工作变得轻松。

Interested in these topics? Follow me on Linkedin or Twitter

对这些主题感兴趣？ 在Linkedin或Twitter上关注我

翻译自: https://medium.com/ai-in-plain-english/ai-has-become-so-human-that-you-cant-tell-the-difference-d62ed2f22775

怎么区分ai人工智能

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